1. Field of the Invention
The present invention relates to a magnetic resonance parallel imaging method, and more particularly to a magnetic resonance parallel imaging method for realizing K-space SENSitivity Encoding, therefore designated KSENSE.
2. Description of the Prior Art
There are many magnetic resonance parallel imaging methods, among which SENSitivity Encoding (SENSE) and GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) are widely recognized to be two relatively successful techniques.
Magnetic resonance parallel imaging techniques are primarily used to solve the problem of image aliasing due to under-sampled signals in k-space. Such aliasing can be expressed as a linear process:b=Ax  (1)where x is an original image to be reconstructed, b is an image with aliasing correction, and A is an aliasing matrix. A is determined by the sensitivity distribution of coils. If A is known, the least-squares solution of the image can be calculated by finding the pseudo-inverse of A:x′=A+b=(AH·A)−1AHb  (2)which has the optimum signal-to-noise ratio (SNR) feature for sensitivity distribution that is measured accurately, where H represents a conjugate transpose operation. This constitutes the basic idea of the known SENSE reconstruction method.
In the radio frequency field, an object being imaged will affect the field distribution of coils as a load, hence generally there is a need for measuring the sensitivity distribution of coils in real time. The sensitivity distribution of the coils is typically a slow function of spatial positions. The auto-calibrating SENSE method obtains the sensitivity distribution of coils in real time by fully sampling in low frequency regions. Of course, the sensitivity distribution obtained in this manner is just an approximation of the actual sensitivity distribution. It should be noted that the sensitivity distribution is meaningful only for positions where signals from the object being measured are present. Therefore, in practical imaging, the high-frequency component of the sensitivity distribution of coils cannot be neglected, but it can not be detected using the technique of measuring the sensitivity by fully sampling in low frequency regions. As a result, at the edge of the object being imaged and in regions where the image phase changes relatively fast, the conventionally determined sensitivity distribution of the coils has a relatively large error. In positions where sensitivity is not accurate, the least-square solution will no longer have the features of optimum SNR and optimal solution. Thus, in such positions and in corresponding positions where aliasing occurs, there may appear a relatively obvious artifact and the SNR is relatively low, and such an artifact develops mainly for the reason that the measurement error of the sensitivity distribution of coils is enlarged during the reconstruction process.
Therefore, the SENSE method, which eliminates such an artifact in the image domain, has the feature of optimum SNR for the sensitivity distribution that is measured relatively accurately. In practice, however, image artifacts tend to appear at positions where the sensitivity distribution has a relatively large error and in corresponding positions where aliasing occurs. A modified SENSE method (mSENSE) based on the same principle has made some improvements in controlling image artifacts, but still fails to obtain satisfactory imaging effects due to limitation of its algorithms.
GRAPPA is typical of another kind of imaging technique for solving the problem of aliasing in k-space. Utilizing the spatial slow-changing characteristic of the sensitivity distribution, such techniques fit the sensitivity distribution of each channel into a spatial harmonic function. Multiplying the harmonic function by the collected signals to fill data that have not been collected has the effect of eliminating artifacts. This kind of technique does not need to calculate the sensitivity distribution of the coils, thereby avoiding image artifacts caused by a calculation error of the sensitivity.
Referring to FIG. 7, which illustrates an embodiment of the GRAPPA technique that fits using four channels (coils 1, 2, 3 and 4), where white points represent under-sampled data that have not been filled, gray points represent reference data that are collected additionally, and black points represent data obtained by intersampling in parallel acceleration mode. GRAPPA employs “black” data to fit “gray” data so as to calculate fitting parameters, and then employs the fitting parameters and black data collected to fit and obtain under-sampled white data. Although the fitting of the spatial harmonic cannot be absolutely accurate, in general, fitting errors in k-space, after being transformed into image domain, will not concentrate at a certain position, while artifact and noise resulting from reconstruction will be distributed in a relatively large region of image. Therefore, in some cases, artifact in images reconstructed using GRAPPA, when being observed visually, are less obvious compared to artifact caused when SENSE is used. Therefore, GRAPPA is more widely used in clinical and practical study.
Although GRAPPA performs all-channel and multiple-harmonic least-squares fitting, such fitting no longer has the feature of optimum SNR in image domain and the fitting parameters are relatively random. Therefore, compared to SENSE, GRAPPA has a lower overall SNR in the image domain.
In addition, in order to eliminate the phase-cancellation effect caused by directly adding multiple channels and to improve the stability of image reconstruction, GRAPPA employs a coil-by-coil fitting scheme, i.e., it performs the fitting operation on the image collected by each channel to remove artifact, and then merges the respective images by calculating the sum of squares (SOS) of moduli of respective images and extracting the square root of SOS. By contrast; SENSE only needs to perform the artifact-eliminating operation on one image. Therefore, although GRAPPA reduces the fitting error and improves the image quality, it requires a longer image reconstruction time and a lower reconstruction speed than SENSE since the time needed to fit full channel data is proportional to the number of channels.
Therefore, a problem in the field of magnetic resonance imaging in need of urgent resolution is to provide a magnetic resonance parallel imaging method which has an optimized SNR, relatively low image artifact and relatively high image reconstruction speed.